Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology wa...

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Vydáno v:	Frontiers in plant science Ročník 13; s. 1047479
Hlavní autoři:	Wu, Qingsong, Xu, Lijia, Zou, Zhiyong, Wang, Jian, Zeng, Qifeng, Wang, Qianlong, Zhen, Jiangbo, Wang, Yuchao, Zhao, Yongpeng, Zhou, Man
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media SA 10.11.2022 Frontiers Media S.A
Témata:	Accuracy Airborne microorganisms Algorithms Cameras Chromatography Classification Crop yield Ensemble learning Food Food quality Germination Humidity Hyperspectral imaging Imaging techniques Legumes Machine learning Mildew mildew detection Mold nondestructive testing Nuts peanut seeds Peanuts Plant Science Real time Seeds Spectrum analysis stacked ensemble learning model variety classification Vision systems Wavelengths nondestructive testing variety classification peanut seeds stacked ensemble learning model mildew detection
ISSN:	1664-462X, 1664-462X
On-line přístup:	Získat plný text
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